Spectacle Frame Recommendation Using Head Shape Clustering
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Solution Overview
Problem
Existing methods for selecting spectacle frames require extensive measurements or detailed information about the person and frames, making the process time-consuming and impractical, especially in online shopping scenarios, and do not guarantee a good anatomical fit without expert intervention.
Innovation Solution
A computer-implemented method for frame recommendation that uses compressed frame and head data clusters, mapped based on similarity criteria, allowing for frame selection without precise measurements or prior knowledge, utilizing principal component analysis and clustering techniques to identify suitable frames for a person's head shape.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If precise 3D scanning and detailed measurements are performed to ensure accurate frame selection, then the anatomical fit quality is improved, but the device complexity and time consumption increase significantly
Solution Approach 1:
The patent uses 2D photographs as simplified copies of the person's head and face instead of requiring complex 3D scans. The image processing system extracts relevant geometric features from these 2D images to create a sufficient representation for frame selection, eliminating the need for expensive and complex 3D scanning hardware while maintaining adequate accuracy for the application
Solution Approach 2:
The patent extracts only the essential geometric features needed for frame selection from the photographs, such as head width, face shape, and key landmark positions. This selective extraction approach obtains sufficient information for accurate frame matching without performing comprehensive and time-consuming full 3D scanning of the entire head
2Manufacturing precision
If comprehensive head measurements and detailed person information are collected to improve frame selection accuracy, then the recommendation quality is improved, but the loss of time and data input burden increase
Solution Approach 1:
The system performs automatic image processing and feature extraction immediately upon receiving the photograph, preparing the head model and geometric features in advance without requiring the user to manually input any measurements. This preliminary automatic processing eliminates time-consuming manual data entry while ensuring all necessary features are captured for accurate frame selection
Solution Approach 2:
The system automatically processes the uploaded photograph to extract all necessary head and face measurements without user intervention. The image processing algorithms automatically identify landmarks, calculate dimensions, and generate the head model, making the system self-sufficient and eliminating the need for users to spend time providing detailed information
3Manufacturing precision
If extensive manual measurements and expert knowledge are required for frame selection, then the anatomical fit is improved, but the ease of operation and accessibility decrease
Solution Approach 1:
The patent replaces manual mechanical measurement processes with automated digital image processing. Instead of requiring users to physically measure their heads or rely on opticians' manual assessment, the system uses computer algorithms to automatically analyze photographs and determine the appropriate frame size and style, making the process accessible to anyone with a smartphone or camera
Solution Approach 2:
The system creates a digital representation of the user's head from a photograph, replacing the need for physical measurements and expert assessment. This digital copy contains all necessary geometric information and can be processed automatically, enabling ordinary users to select frames independently without requiring expert knowledge or manual measurement skills
4Manufacturing precision
If detailed frame specifications and multiple frame options are analyzed to improve selection quality, then the recommendation accuracy is improved, but the productivity and processing speed decrease
Solution Approach 1:
The patent divides the frame selection process into distinct segments: image processing, feature extraction, head model creation, frame database matching, and recommendation generation. This segmentation allows each component to be optimized independently, processing only the necessary features and comparing them against relevant frame parameters, thereby maintaining high accuracy while improving overall processing efficiency
Solution Approach 2:
The system transforms the frame selection problem into a parameter-matching problem by extracting key geometric parameters from the photograph and comparing them with stored frame specifications. This parameter-based approach converts a complex visual assessment into efficient numerical comparisons, significantly speeding up the processing while maintaining accurate matching based on critical dimensions
Data Source
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AI summary
Methods and devices related to spectacle frame recommendation are provided. For configuring a device (10) for frame recommendation, frame data is clustered to provide a plurality of frame data clusters (51A, 51B, 51C), and head data is clustered to provide a plurality of head data clusters (61A, 61B, 61C, 61D). A mapping between the head data clusters (61A, 61B, 61C, 61D) and the frame data clusters (51A, 51B, 51C) is provided. For recommendation of a frame to a person, head data of the person is obtained, and a head data cluster is identified based on the head data. Based on the identified head data cluster and the mapping, then a frame data cluster is selected which forms the basis for the recommendation.